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HISTAI: An Open-Source, Large-Scale Whole Slide Image Dataset for Computational Pathology

arXiv 2505.12120 ↗First posted 17 May 2025eess.IV cs.CVcited by 14

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Abstract (arXiv)

Recent advancements in Digital Pathology (DP), particularly through artificial intelligence and Foundation Models, have underscored the importance of large-scale, diverse, and richly annotated datasets. Despite their critical role, publicly available Whole Slide Image (WSI) datasets often lack sufficient scale, tissue diversity, and comprehensive clinical metadata, limiting the robustness and generalizability of AI models. In response, we introduce the HISTAI dataset, a large, multimodal, open-access WSI collection comprising over 60,000 slides from various tissue types. Each case in the HISTAI dataset is accompanied by extensive clinical metadata, including diagnosis, demographic information, detailed pathological annotations, and standardized diagnostic coding. The dataset aims to fill gaps identified in existing resources, promoting innovation, reproducibility, and the development of clinically relevant computational pathology solutions. The dataset can be accessed at https://github.com/HistAI/HISTAI.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

3 of 8 references.

References · 3

  1. 2018Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challengecited by 61
  2. 2024Hibou: A Family of Foundational Vision Transformers for Pathologycited by 57
  3. 2025A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Taskscited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 12.

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  1. 2025Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Imagescited by 1
  2. 2026A Generative Foundation Model for Multimodal Histopathologycited by 1
  3. 2026Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Predictioncited by 1
  4. 2025StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathologycited by 0
  5. 2026PathoScribe: Transforming Pathology Data into a Living Library with a Unified LLM-Driven Framework for Semantic Retrieval and Clinical Integrationcited by 0
  6. 2026A Breast Vision Pathology Foundation Model for Real-world Clinical Utilitycited by 0
  7. 2026Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generationcited by 0
  8. 2026A Pathology Foundation Model for Gastric Cancer with Real-World Validationcited by 0
  9. 2026DaX: Learning General Pathology Representations Across Scalescited by 0
  10. 2026ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Expertscited by 0
  11. 2026InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysiscited by 0
  12. 2026Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generationcited by 0
2 more
  1. 2026Lumen: Parameter-Efficient Alignment of Pretrained Vision and Language Encoders for Zero-Shot Computational Pathologycited by 0
  2. 2026Beyond Sparse Captions: Aligning Slide-Level Text and Patch-Level Vision in Pathologycited by 0